A numerical framework for operational coupled fire-atmosphere-fuel moisture-smoke forecasting
Presented by: CIRA
Hosted by: Jan Mandel, University of Colorado Denver (presenting) Adam Kochanski, University of Utah Sher Schranz, CIRA Martin Vejmelka, AVAST
Date: October 25, 2018 11:00 am
Location: CIRA Directors Conference Room
We present an integrated wildland fire model WRF-SFIRE, based on combining a high resolution, multi-scale weather forecasting model WRF, with a semi-empirical fire spread model, a prognostic dead fuel moisture model, and smoke dispersion. Fire-released heat and moisture impact local meteorology. The fuel moisture model is driven by the atmospheric component of the system in order to render the diurnal and spatial fuel moisture variability. The dead fuel moisture is traced in three different fuel classes (1h, 10h and 100h fuel), which are combined to provide the total dead fuel moisture content at the fire model resolution (tens of meters) using fuel properties at the location. The wind and the fuel moisture in turn impact the fire rate of spread. The sub-kilometer model resolution enables detailed representation of complex terrain, and small-scale variability in surface properties. Ingest of infrared fire perimeters is supported by an interpolation of the fire arrival time between the perimeter and a previous one to spin up the atmosphere model.
The fire simulations are run in online system WRFx. The simulations are initialized by a web-based control system allowing a user to define a fire as well as basic simulation properties such as simulation length, type of meteorological forcing and resolution, anywhere in CONUS and any time meteorological products are available to initialize the weather model. The data is downloaded automatically, and the system monitors execution on a cluster. The simulation results are processed while the model is running and displayed as animations on a web visualization portal.
The portal also provides nationwide nowcasting of fuel moisture, based on the fuel moisture model with assimilation of surface observations of the fuel moisture from RAWS stations.